1 00:00:02,640 --> 00:00:05,320 Speaker 1: Welcome to the Bloomberg Penel podcast. I'm Paul swing you 2 00:00:05,360 --> 00:00:07,680 Speaker 1: along with my co host Lisa Brahma Waits. Each day 3 00:00:07,720 --> 00:00:10,240 Speaker 1: we bring you the most noteworthy and useful interviews for 4 00:00:10,280 --> 00:00:12,520 Speaker 1: you and your money. Whether at the grocery store or 5 00:00:12,560 --> 00:00:15,480 Speaker 1: the trading floor. Find a Bloomberg Penl podcast on Apple 6 00:00:15,520 --> 00:00:17,959 Speaker 1: podcast or wherever you listen to podcasts, as well as 7 00:00:17,960 --> 00:00:21,120 Speaker 1: at Bloomberg dot com. You know, when Amazon bought Whole 8 00:00:21,160 --> 00:00:23,759 Speaker 1: Foods for I think fourteen or fifteen billion dollars, I 9 00:00:23,840 --> 00:00:25,640 Speaker 1: thought to myself at the time, why would they want 10 00:00:25,640 --> 00:00:29,120 Speaker 1: to get into the grocery business. It's a low margin, 11 00:00:29,720 --> 00:00:33,080 Speaker 1: highly intensely competitive business. Why would they want to, you know, 12 00:00:33,120 --> 00:00:35,360 Speaker 1: kind of get away from their cool tech and home 13 00:00:35,400 --> 00:00:37,800 Speaker 1: delivery and all that kind of stuff. But they're there. 14 00:00:37,920 --> 00:00:39,640 Speaker 1: They are sticking with it, and I think they're actually 15 00:00:39,640 --> 00:00:43,920 Speaker 1: increasing their investments and that's forcing the industry, the supermarket industry, 16 00:00:43,920 --> 00:00:47,160 Speaker 1: to respond with their own technology. Matthew Boyle, us retail 17 00:00:47,200 --> 00:00:49,240 Speaker 1: reporter for Bloomberg News, joins us live here in the 18 00:00:49,240 --> 00:00:52,839 Speaker 1: Bloomberg and Director Broker studio to help us break it down. So, Matthew, 19 00:00:52,840 --> 00:00:55,880 Speaker 1: give us a sense of what the supermarket industry is 20 00:00:55,960 --> 00:01:00,720 Speaker 1: doing to try to fend off or compete with Amazon. Uh, well, 21 00:01:01,240 --> 00:01:02,960 Speaker 1: they need to do something. I mean, they're in a 22 00:01:03,000 --> 00:01:05,119 Speaker 1: war of attrition right now and they're losing. And I'm 23 00:01:05,120 --> 00:01:09,000 Speaker 1: talking about traditional supermarkets. You know, um places like Kroger, 24 00:01:09,200 --> 00:01:12,800 Speaker 1: which just earlier this year had to throw out their 25 00:01:12,840 --> 00:01:16,640 Speaker 1: long term guidance because they couldn't get a story remodel. Right, 26 00:01:16,800 --> 00:01:19,640 Speaker 1: So you're talking about traditional supermarkets that are getting squeezed, 27 00:01:19,680 --> 00:01:22,080 Speaker 1: and not just by Amazon, by companies like all the 28 00:01:22,520 --> 00:01:25,200 Speaker 1: this German import which is very deep discount what they 29 00:01:25,240 --> 00:01:27,759 Speaker 1: call hard discounters, where it's a lot of store brands 30 00:01:27,959 --> 00:01:30,720 Speaker 1: and a very spartan store experience. But people don't mind 31 00:01:30,760 --> 00:01:32,400 Speaker 1: if you've give them what they want at a price 32 00:01:32,480 --> 00:01:34,920 Speaker 1: you know, that's affordable. They don't need a lot of 33 00:01:34,959 --> 00:01:37,320 Speaker 1: the bells and whistles. So the grocers are kind of 34 00:01:37,360 --> 00:01:40,479 Speaker 1: getting it from all sides. The traditional grocery stores. They 35 00:01:40,520 --> 00:01:43,320 Speaker 1: need to think differently, and so they're looking to technology, 36 00:01:43,400 --> 00:01:46,080 Speaker 1: some of which Amazon is has already adopted or is 37 00:01:46,120 --> 00:01:49,560 Speaker 1: adopting as ways to not just you know, make shoppers 38 00:01:49,600 --> 00:01:51,840 Speaker 1: go wow, look at all these funky bells and whistles, 39 00:01:52,000 --> 00:01:54,360 Speaker 1: but just to sort of get the basics right and 40 00:01:54,440 --> 00:01:58,200 Speaker 1: solve some operational problems that will hopefully lift those margins 41 00:01:58,200 --> 00:02:00,320 Speaker 1: that you mentioned that are always a razor thing. You 42 00:02:00,360 --> 00:02:02,720 Speaker 1: also suggested that perhaps they could hide under a desk 43 00:02:02,760 --> 00:02:05,360 Speaker 1: in the fetal position, but that might not be the 44 00:02:05,400 --> 00:02:08,960 Speaker 1: best business Other industries have industries have done that, and uh, 45 00:02:09,080 --> 00:02:11,239 Speaker 1: you know, it is a business model that has been adapted, 46 00:02:11,360 --> 00:02:14,359 Speaker 1: not necessarily successfully. One statistic in your story that I 47 00:02:14,360 --> 00:02:17,119 Speaker 1: thought was really compelling was that food retailers globally lose 48 00:02:17,160 --> 00:02:21,440 Speaker 1: about three billion dollars a year due to items being 49 00:02:21,520 --> 00:02:25,000 Speaker 1: out of stock. Is this the type of technological challenge 50 00:02:25,720 --> 00:02:28,280 Speaker 1: that could just be fixed by having the right program 51 00:02:28,280 --> 00:02:30,000 Speaker 1: that understands what you have. I mean, I don't want 52 00:02:30,040 --> 00:02:32,239 Speaker 1: to say fixed, but it will certainly help. I mean, 53 00:02:32,320 --> 00:02:34,680 Speaker 1: what is easier? You know you've got these days you 54 00:02:34,760 --> 00:02:38,440 Speaker 1: have to hire people to literally walk down the aisles. 55 00:02:38,480 --> 00:02:41,160 Speaker 1: It could take hours and take them away from other tasks, 56 00:02:41,240 --> 00:02:43,800 Speaker 1: let's say, like filling an online order and getting that 57 00:02:43,880 --> 00:02:46,760 Speaker 1: out to a shopper and doing something more value added. 58 00:02:47,040 --> 00:02:50,000 Speaker 1: These robots that we talk about in the story that 59 00:02:50,160 --> 00:02:53,359 Speaker 1: just sort of meander up and down the aisles checking 60 00:02:53,440 --> 00:02:56,840 Speaker 1: for out of stocks, for missing items in the company. 61 00:02:56,880 --> 00:02:58,840 Speaker 1: I spoke to a Giant Eagle, which is, you know, 62 00:02:58,919 --> 00:03:02,760 Speaker 1: a great Pittsburgh, Midwestern based retailer. Uh. They saw a 63 00:03:02,840 --> 00:03:06,040 Speaker 1: twenty one percent reduction in out of stocks in the 64 00:03:06,120 --> 00:03:08,200 Speaker 1: one store where they've had this robot the longest I 65 00:03:08,240 --> 00:03:11,400 Speaker 1: gotta say in this, in this technological revolution that we're in, 66 00:03:11,919 --> 00:03:14,840 Speaker 1: the problems that people are discovering, and the solutions stock 67 00:03:14,919 --> 00:03:17,560 Speaker 1: the shelves. Yeah, I mean literally it's you know, how 68 00:03:17,639 --> 00:03:20,280 Speaker 1: much how much money and how much time can it 69 00:03:20,320 --> 00:03:24,720 Speaker 1: be just put into the idea of better Yeah, exactly. So, 70 00:03:24,760 --> 00:03:27,840 Speaker 1: Matthew Canney, I'm thinking about, you know, walking down the 71 00:03:27,880 --> 00:03:30,519 Speaker 1: aisle of a supermarket and seeing that worker with the 72 00:03:30,639 --> 00:03:32,959 Speaker 1: kind of the price gun. Do you know kind of 73 00:03:33,600 --> 00:03:35,240 Speaker 1: are those days can you do that one more time? 74 00:03:35,720 --> 00:03:41,520 Speaker 1: That sound? Did you? Are those days still with us? 75 00:03:41,520 --> 00:03:43,839 Speaker 1: Things like which I didn't even write about, But those 76 00:03:43,960 --> 00:03:47,640 Speaker 1: robots we mentioned, if you add electronic shelf labels or 77 00:03:47,640 --> 00:03:52,800 Speaker 1: electronic shelf tags to replace the sticker gun, um, that 78 00:03:52,840 --> 00:03:56,480 Speaker 1: can that will benefit even more so the use of 79 00:03:56,520 --> 00:04:00,280 Speaker 1: those shelf stocking robots when you just have electronic shelf X. 80 00:04:00,400 --> 00:04:02,720 Speaker 1: So there's a lot of them, but again it's slow adoption. 81 00:04:02,880 --> 00:04:04,960 Speaker 1: A big problem here is that the grocers are very 82 00:04:05,080 --> 00:04:07,120 Speaker 1: risk averse. They want to do, you know, what their 83 00:04:07,200 --> 00:04:09,240 Speaker 1: dad did, their grandfather did. This is the way we've 84 00:04:09,280 --> 00:04:11,720 Speaker 1: always run this store. Um, you know, this is what's 85 00:04:11,720 --> 00:04:14,360 Speaker 1: worked in the past. But they again, they're starting to 86 00:04:14,400 --> 00:04:17,760 Speaker 1: get a little bit more adventurous here. It's more than that, though. 87 00:04:17,800 --> 00:04:20,599 Speaker 1: I mean a lot of these companies operate on pretty 88 00:04:20,600 --> 00:04:23,840 Speaker 1: small margins, right. Uh, this is not a get rich 89 00:04:23,920 --> 00:04:27,120 Speaker 1: quick kind of business, and you have to make investment 90 00:04:27,120 --> 00:04:30,239 Speaker 1: in order to succeed against the Walmarts of the world 91 00:04:30,320 --> 00:04:32,279 Speaker 1: or the Amazons of the world. So can you sort 92 00:04:32,279 --> 00:04:35,120 Speaker 1: of give us a sense of how they're doing it, 93 00:04:35,279 --> 00:04:38,159 Speaker 1: how they're investing, if they're able to invest given the 94 00:04:38,200 --> 00:04:40,320 Speaker 1: overhang of their little Exactly, you're rightly, this is not 95 00:04:40,400 --> 00:04:42,839 Speaker 1: get rich quick, this is not die. This is don't 96 00:04:42,839 --> 00:04:46,119 Speaker 1: go away, don't become the next year's So they're making 97 00:04:46,120 --> 00:04:50,440 Speaker 1: investments that exactly you know, they have to make. But 98 00:04:50,480 --> 00:04:52,360 Speaker 1: a the same time, look at Walmart, they're they're also 99 00:04:52,440 --> 00:04:54,920 Speaker 1: paying all of their workers more. You know, they've had 100 00:04:54,960 --> 00:04:57,440 Speaker 1: to increase just their starting minimum wage and in many 101 00:04:57,440 --> 00:04:59,920 Speaker 1: cities New York included. You know, if you're talking about 102 00:04:59,920 --> 00:05:02,320 Speaker 1: a fifteen dollar minim waves. So they are making investments 103 00:05:02,320 --> 00:05:05,680 Speaker 1: in their people, but these investments in technology, which are 104 00:05:05,760 --> 00:05:08,280 Speaker 1: increasing what they will not make them though if they 105 00:05:08,279 --> 00:05:11,080 Speaker 1: don't get a clear return on investment. That's why a 106 00:05:11,120 --> 00:05:13,360 Speaker 1: lot of what we're seeing just now is just pilots, 107 00:05:13,560 --> 00:05:16,560 Speaker 1: a couple of stores here, twenty stores there, figure out 108 00:05:16,600 --> 00:05:18,600 Speaker 1: what's working tweak it. So we're not going to see 109 00:05:18,720 --> 00:05:22,400 Speaker 1: wide scale universal adoption of a lot of these technologies 110 00:05:22,520 --> 00:05:25,320 Speaker 1: for probably years, and there have been technologies over the 111 00:05:25,360 --> 00:05:28,200 Speaker 1: past five years that we thought we're going to change 112 00:05:28,200 --> 00:05:30,200 Speaker 1: the way we shopped, and they haven't because it was 113 00:05:30,240 --> 00:05:32,640 Speaker 1: a lot of g whiz stuff that didn't really provide 114 00:05:32,640 --> 00:05:35,640 Speaker 1: an r O. I. So Amazon again, they I call 115 00:05:35,680 --> 00:05:38,760 Speaker 1: it dipping their toe with this Whole Foods acquisition, dipping 116 00:05:38,800 --> 00:05:41,640 Speaker 1: their toe into the supermarket business. Is there any sense 117 00:05:41,640 --> 00:05:44,080 Speaker 1: that they're gonna maybe do more than dip their toe. Yeah, 118 00:05:44,120 --> 00:05:45,640 Speaker 1: I think we've got a foot in there now. It's 119 00:05:45,680 --> 00:05:48,200 Speaker 1: more than a toe, given you know, it's started with 120 00:05:48,240 --> 00:05:51,039 Speaker 1: Whole Foods, of course, but just in recent weeks we've 121 00:05:51,040 --> 00:05:53,000 Speaker 1: had a lot of news from them. They are going 122 00:05:53,040 --> 00:05:56,800 Speaker 1: to open a traditional, more traditional, lower priced than Whole 123 00:05:56,839 --> 00:06:00,400 Speaker 1: foods Uh grocery chain. Starting in l A. They have 124 00:06:00,560 --> 00:06:04,320 Speaker 1: slashed or eliminated the additional fee that they charge for 125 00:06:04,360 --> 00:06:07,280 Speaker 1: their prime customers to do Amazon Fresh, which is their 126 00:06:07,320 --> 00:06:10,520 Speaker 1: online service, essentially saying, if you're a Prime customer, now 127 00:06:10,600 --> 00:06:13,479 Speaker 1: the food delivery is free, just as the streaming video 128 00:06:13,760 --> 00:06:16,560 Speaker 1: is free. So and they're also planning to take their 129 00:06:16,600 --> 00:06:19,919 Speaker 1: Amazon Go technology, the cashier list stores we all have 130 00:06:20,040 --> 00:06:22,560 Speaker 1: heard about, and bring that to bigger stores rather than 131 00:06:22,600 --> 00:06:25,160 Speaker 1: the tiny ones. What's that smell? It's the smell of 132 00:06:25,640 --> 00:06:28,760 Speaker 1: burning cash. I mean, I'm listening to you speak. Where 133 00:06:28,839 --> 00:06:31,240 Speaker 1: is the make money part? Well? For Amazon, you know 134 00:06:31,240 --> 00:06:35,080 Speaker 1: they're making money off the cloud and advertising. Yeah, so 135 00:06:35,120 --> 00:06:38,359 Speaker 1: that's why again we're seeing very selected small pilots. But 136 00:06:38,400 --> 00:06:40,800 Speaker 1: with the things like the shelf scanning robots don't look 137 00:06:40,800 --> 00:06:43,120 Speaker 1: at it of out of stocks, that's such jargon e term. 138 00:06:43,240 --> 00:06:46,440 Speaker 1: Think of it as lost sales. If the honey bear 139 00:06:46,560 --> 00:06:48,719 Speaker 1: isn't there on the shelf, you're not getting that sale 140 00:06:48,720 --> 00:06:50,920 Speaker 1: of honey If it is there, If the robot tells 141 00:06:50,960 --> 00:06:53,480 Speaker 1: you it's not there, get you know, get somebody to 142 00:06:53,520 --> 00:06:55,679 Speaker 1: get it in the back. You have now gained a sale. 143 00:06:56,040 --> 00:06:58,839 Speaker 1: That is a sale that is profits certainly, so that 144 00:06:59,040 --> 00:07:01,360 Speaker 1: is what will help these I certainly, rather than all 145 00:07:01,360 --> 00:07:04,040 Speaker 1: these fancy terms like AI and VR and stuff like that. 146 00:07:04,080 --> 00:07:06,640 Speaker 1: If the money bear ain't there, the honey bear ain't there. 147 00:07:06,520 --> 00:07:09,359 Speaker 1: Here's the revelation for you. I actually enjoy food shopping. 148 00:07:09,920 --> 00:07:12,320 Speaker 1: But I have to tell you this. If there was 149 00:07:12,360 --> 00:07:14,880 Speaker 1: an app to say, hey, where is the honey bear? Oh, 150 00:07:14,920 --> 00:07:18,880 Speaker 1: it's all three road to you know, ten steps that 151 00:07:19,080 --> 00:07:22,400 Speaker 1: I actually looking. Most good retailers will do that. If 152 00:07:22,400 --> 00:07:24,600 Speaker 1: you have their shopping app and you walk in and 153 00:07:24,760 --> 00:07:26,280 Speaker 1: a lot of them will allow you to upload your 154 00:07:26,280 --> 00:07:29,280 Speaker 1: shopping list. There will be a map overlaid. It'll know 155 00:07:29,280 --> 00:07:31,280 Speaker 1: where your location is, and I will say, the honey 156 00:07:31,280 --> 00:07:34,640 Speaker 1: bear is here. Um. You know that's what really screws 157 00:07:34,640 --> 00:07:37,280 Speaker 1: people up. When a store gets remodeled, people are suddenly saying, 158 00:07:37,360 --> 00:07:40,640 Speaker 1: where's the honey bear? I will say. One thing I 159 00:07:40,640 --> 00:07:44,520 Speaker 1: think would be really cool being app showing the expiration 160 00:07:44,680 --> 00:07:47,560 Speaker 1: dates of different things. So that's a totally different topic. 161 00:07:47,680 --> 00:07:50,760 Speaker 1: You know, the sell by used by that's we'll have 162 00:07:50,840 --> 00:07:52,880 Speaker 1: to continue that. We'll have to have a full show 163 00:07:52,960 --> 00:07:55,960 Speaker 1: on the sell by and used by dates. Matthew Boyle, 164 00:07:55,960 --> 00:07:57,240 Speaker 1: thank you so much for being with us. That was 165 00:07:57,240 --> 00:07:59,600 Speaker 1: a great story. Matt Boyle, as the US retail reporter 166 00:07:59,760 --> 00:08:04,040 Speaker 1: for Bloomberg News, joining us here in our interactive broker studios. 167 00:08:04,560 --> 00:08:07,880 Speaker 1: Really interesting revolution under way in the groceries, not where 168 00:08:07,920 --> 00:08:24,520 Speaker 1: the industry can keep up. Boy US shopper spent a 169 00:08:24,640 --> 00:08:28,119 Speaker 1: record at nine point two billion dollars on Cyber Monday. 170 00:08:28,120 --> 00:08:31,280 Speaker 1: That's sevent more than last year. It added to a 171 00:08:31,360 --> 00:08:34,280 Speaker 1: robust Black Friday. So it seems like the consumer is 172 00:08:34,400 --> 00:08:36,680 Speaker 1: in good shape as we head into the thick of 173 00:08:36,679 --> 00:08:39,640 Speaker 1: the holiday sales. To get more color, we welcome Christian Magoon. 174 00:08:39,920 --> 00:08:43,280 Speaker 1: Christian is a chief executive officers Amplify e t S 175 00:08:43,360 --> 00:08:46,160 Speaker 1: with over seven hundred fifty million dollars and under management 176 00:08:46,160 --> 00:08:50,160 Speaker 1: based in Colorado Springs. Christian, thanks so much for joining us. 177 00:08:50,200 --> 00:08:53,600 Speaker 1: So it seems like the consumer is out there spending 178 00:08:53,640 --> 00:08:57,480 Speaker 1: for the holidays. Yeah, Paul, has been definitely a very 179 00:08:57,520 --> 00:09:01,040 Speaker 1: good start to the season. Um, We've likely to see 180 00:09:01,160 --> 00:09:05,200 Speaker 1: record holiday shopping. Um. You know, some forecasts believe that 181 00:09:05,240 --> 00:09:08,280 Speaker 1: this year will grow about four percent overall and holiday 182 00:09:08,280 --> 00:09:10,920 Speaker 1: shopping and actually go from about a nine hundred and 183 00:09:10,960 --> 00:09:15,400 Speaker 1: seventy billion dollar season last year to a trillion dollars 184 00:09:15,480 --> 00:09:18,840 Speaker 1: season this year, so very exciting. I think, you know, 185 00:09:18,880 --> 00:09:23,440 Speaker 1: fifty year unemployment, steady wage growth are really helping the 186 00:09:23,480 --> 00:09:27,120 Speaker 1: consumer be confident. Here. Of course, the big area of 187 00:09:27,160 --> 00:09:30,880 Speaker 1: growth has been online retail. UM. Well, total sales maybe 188 00:09:30,920 --> 00:09:34,360 Speaker 1: up about four percent. Online retail is trending up maybe 189 00:09:34,480 --> 00:09:37,480 Speaker 1: fifteen to sixteen percent. So that's going to be the 190 00:09:37,520 --> 00:09:41,160 Speaker 1: sweet spot for investors and we think for those looking 191 00:09:41,160 --> 00:09:45,000 Speaker 1: for to kind of ride this retail trend, both you know, 192 00:09:45,080 --> 00:09:47,720 Speaker 1: for growth, but also for kind of the trend of 193 00:09:47,880 --> 00:09:50,920 Speaker 1: going online versus in store. Christian, I'm looking right now 194 00:09:50,960 --> 00:09:53,160 Speaker 1: at the holdings of I Buy, which is the e 195 00:09:53,280 --> 00:09:56,320 Speaker 1: t F that you run, uh with about two billion 196 00:09:56,320 --> 00:09:59,719 Speaker 1: dollars of assets over under management. I'm just looking. Hell, 197 00:09:59,800 --> 00:10:02,079 Speaker 1: let's one is that is that a retail stock that's 198 00:10:02,080 --> 00:10:05,000 Speaker 1: your top holding according to this That's right, So it's 199 00:10:05,040 --> 00:10:07,240 Speaker 1: a newly added member of the e t F. And 200 00:10:07,320 --> 00:10:09,959 Speaker 1: really the criteria, Lisa, is that a company has to 201 00:10:10,000 --> 00:10:14,360 Speaker 1: have seventy or more of their revenue coming from online sales. 202 00:10:14,600 --> 00:10:18,680 Speaker 1: And Peloton fits that that criteria. Um, you know, most 203 00:10:18,679 --> 00:10:21,080 Speaker 1: people think of Amazon as being you know, kind of 204 00:10:21,080 --> 00:10:24,760 Speaker 1: the primary online retailer, but you know, Amazon is just 205 00:10:24,840 --> 00:10:28,800 Speaker 1: one of many. We've actually had more performance in alpha 206 00:10:28,840 --> 00:10:33,560 Speaker 1: generated in the last year from companies like Carbona, Shopify, 207 00:10:33,920 --> 00:10:36,640 Speaker 1: v I P Shop, all those companies up between a 208 00:10:36,720 --> 00:10:40,040 Speaker 1: hundred and thirty and a hundred this year. UM. You know, 209 00:10:40,240 --> 00:10:42,880 Speaker 1: I buy is unique from an online retail ETF because 210 00:10:42,920 --> 00:10:45,800 Speaker 1: it is not market cap weighted. It's equal weighted UM 211 00:10:45,880 --> 00:10:48,040 Speaker 1: and we do have that revenue test, so we think 212 00:10:48,080 --> 00:10:50,760 Speaker 1: that you're getting access to a lot of unique names 213 00:10:50,760 --> 00:10:54,440 Speaker 1: that maybe don't necessarily know. I think Peloton is one 214 00:10:54,480 --> 00:10:56,760 Speaker 1: that people know right now, but the carbon is of 215 00:10:56,800 --> 00:11:00,280 Speaker 1: the world, v I P Shop, Okado, those name tims 216 00:11:00,240 --> 00:11:02,600 Speaker 1: to have a nice impact and have been an alpha 217 00:11:02,679 --> 00:11:04,960 Speaker 1: driver for the fund, which is a five star rated 218 00:11:04,960 --> 00:11:06,839 Speaker 1: morning Star fund in the number one performer in the 219 00:11:06,880 --> 00:11:10,080 Speaker 1: consumer cyclical category over the last three years. I will 220 00:11:10,080 --> 00:11:12,840 Speaker 1: just say, Paul, it's interesting to see what retail online 221 00:11:12,840 --> 00:11:16,320 Speaker 1: retail consists of. It consists of food and getting cars 222 00:11:16,320 --> 00:11:18,440 Speaker 1: to take to places, right, I mean, it's it's grub Hub, 223 00:11:18,840 --> 00:11:20,839 Speaker 1: it's getting lift uber and then and then when you 224 00:11:20,880 --> 00:11:23,280 Speaker 1: feel really guilty about not moving around, you go bike 225 00:11:23,520 --> 00:11:28,920 Speaker 1: and covered. Hey, Christian, so I know there's one less 226 00:11:29,240 --> 00:11:32,760 Speaker 1: shopping week here for this holiday period relatutil last year. 227 00:11:33,000 --> 00:11:35,040 Speaker 1: What kind of risk is that for some of these 228 00:11:35,040 --> 00:11:37,800 Speaker 1: retailers and some of the ETFs that track them. Yeah, 229 00:11:37,800 --> 00:11:39,760 Speaker 1: it definitely is a risk. I mean, we're six days 230 00:11:39,800 --> 00:11:43,160 Speaker 1: less because the holiday shopping season started later, with Thanksgiving 231 00:11:43,200 --> 00:11:45,680 Speaker 1: being six days later than last year. So you know, 232 00:11:45,720 --> 00:11:48,880 Speaker 1: one risk is bad weather, frankly, because we have a 233 00:11:48,880 --> 00:11:51,960 Speaker 1: compressed time period, and we've actually now seen that a 234 00:11:51,960 --> 00:11:54,480 Speaker 1: little bit on Black Friday and Cyber Monday. Thank you 235 00:11:54,559 --> 00:11:57,599 Speaker 1: guys experienced it yesterday a little bit. And the interesting 236 00:11:57,679 --> 00:12:00,920 Speaker 1: stat we're seeing from Adobe Analytics, which tracks the hundred 237 00:12:01,040 --> 00:12:04,920 Speaker 1: largest online retailers, is that uh, states that had bad 238 00:12:04,960 --> 00:12:08,160 Speaker 1: weather two inches or more of snow solid uptick and 239 00:12:08,200 --> 00:12:11,720 Speaker 1: online retail sales by between uh you know, seven and 240 00:12:11,840 --> 00:12:14,560 Speaker 1: nine per cent. So we're seeing that, um, you know, 241 00:12:14,600 --> 00:12:17,240 Speaker 1: brick and mortar kind of face this headline risk of 242 00:12:17,320 --> 00:12:20,360 Speaker 1: bad weather where people stay in, but it actually turns 243 00:12:20,400 --> 00:12:24,200 Speaker 1: into a tail wind for online retailers. Um. Also, you know, 244 00:12:24,240 --> 00:12:27,200 Speaker 1: this this trade issue is definitely something that could impact 245 00:12:27,280 --> 00:12:29,839 Speaker 1: some consumer confidence. In the last four months, we've seen 246 00:12:29,880 --> 00:12:33,280 Speaker 1: it trending downward, still in a very healthy range, but 247 00:12:33,679 --> 00:12:36,560 Speaker 1: it's something to watch, particularly as we've heard President Trump 248 00:12:36,600 --> 00:12:38,760 Speaker 1: back away from maybe the urgency of doing a trade 249 00:12:38,800 --> 00:12:41,360 Speaker 1: deal here by the end of the year. Christian, you 250 00:12:41,400 --> 00:12:44,080 Speaker 1: talked about the good performance of I Buy over the 251 00:12:44,080 --> 00:12:46,880 Speaker 1: past three years, and certainly the shares of stocks that 252 00:12:46,920 --> 00:12:48,760 Speaker 1: you have, the shares of companies that you have in 253 00:12:48,760 --> 00:12:53,079 Speaker 1: your portfolio have done very well, the likes of Expedia 254 00:12:53,280 --> 00:12:58,480 Speaker 1: or let's see lift Uber Netflix, That however, is getting 255 00:12:58,480 --> 00:13:01,240 Speaker 1: called into question now because of how high the valuations 256 00:13:01,320 --> 00:13:04,240 Speaker 1: are and this question of yes, this is the new model, 257 00:13:04,320 --> 00:13:06,840 Speaker 1: but perhaps there has been too much capital put into 258 00:13:06,840 --> 00:13:11,160 Speaker 1: these particular companies. How do you respond to the valuation questions. Well, 259 00:13:11,160 --> 00:13:13,800 Speaker 1: it's definitely a challenge because when you look at maybe 260 00:13:13,800 --> 00:13:16,960 Speaker 1: the counterparts of brick and mortar, they have very low valuations, 261 00:13:17,000 --> 00:13:19,640 Speaker 1: but of course there's the risk they're going out of business. 262 00:13:19,880 --> 00:13:23,679 Speaker 1: So when we look at the valuations of these growth companies, 263 00:13:23,960 --> 00:13:27,320 Speaker 1: you know, their PEG ratios are we think are still attractive. 264 00:13:27,600 --> 00:13:30,520 Speaker 1: You know, right now there's still about eleven percent market 265 00:13:30,520 --> 00:13:34,040 Speaker 1: share of online retail as opposed to all retail sales 266 00:13:34,080 --> 00:13:36,880 Speaker 1: in the US, uh in China, for example, that's over 267 00:13:37,760 --> 00:13:40,439 Speaker 1: market share. We think many of these companies are going 268 00:13:40,480 --> 00:13:42,960 Speaker 1: to double or triple their market share or their sales 269 00:13:43,040 --> 00:13:45,960 Speaker 1: over the next three to five years as online retail 270 00:13:46,400 --> 00:13:49,480 Speaker 1: starts to continues to emerge. Right now, going back to 271 00:13:50,760 --> 00:13:55,080 Speaker 1: online retail has gone about an average compounded annual growth rate. 272 00:13:55,280 --> 00:13:57,400 Speaker 1: So when we look at these companies, we actually think 273 00:13:57,440 --> 00:13:58,960 Speaker 1: that over the next three to five years, if they 274 00:13:58,960 --> 00:14:02,880 Speaker 1: double or triple their their sales, that these these valuations 275 00:14:02,880 --> 00:14:07,040 Speaker 1: will actually look potentially like values. Um. We just think 276 00:14:07,080 --> 00:14:11,080 Speaker 1: this trend is going to continue. More and more consumers 277 00:14:11,280 --> 00:14:14,880 Speaker 1: are going to trust going online, whether that's through mobile 278 00:14:14,880 --> 00:14:18,959 Speaker 1: payments or the convenience, the competitive pricing, or the or 279 00:14:19,000 --> 00:14:21,680 Speaker 1: the increased selection. So we think this is a global 280 00:14:21,680 --> 00:14:24,320 Speaker 1: trend that investors can capitalize. And you know, since the 281 00:14:24,360 --> 00:14:27,520 Speaker 1: fund has been out, it's returned about over the last 282 00:14:27,560 --> 00:14:29,600 Speaker 1: three and a half years versus the SMP at about 283 00:14:29,600 --> 00:14:32,280 Speaker 1: fift so it's definitely been a place for Elphin. We 284 00:14:32,440 --> 00:14:34,480 Speaker 1: think that's going to continue. Christian mgoon, thank you so 285 00:14:34,560 --> 00:14:37,120 Speaker 1: much for being with us. Christian mcgoon, chief executive officer 286 00:14:37,160 --> 00:14:56,200 Speaker 1: of Amplify E t F. It's all about trade today. 287 00:14:56,240 --> 00:14:58,360 Speaker 1: That's what's thinking at stocks. At least if you trust 288 00:14:58,640 --> 00:15:01,840 Speaker 1: the price action in responds to certain headlines President Trump 289 00:15:01,840 --> 00:15:03,920 Speaker 1: coming out and saying, who knows, Maybe we'll make a deal, 290 00:15:03,960 --> 00:15:05,840 Speaker 1: maybe we won't, maybe we don't need to make a 291 00:15:05,840 --> 00:15:09,200 Speaker 1: deal before election, and I'll push it back after that. 292 00:15:09,560 --> 00:15:12,280 Speaker 1: You're seeing the nastack down one point two percent. Also, 293 00:15:12,320 --> 00:15:15,440 Speaker 1: now we're hearing about taxes on porcelain and French wine, 294 00:15:15,640 --> 00:15:18,480 Speaker 1: and she's joining us on to discuss all things trade. 295 00:15:18,480 --> 00:15:21,720 Speaker 1: Brendan Murray, who covers the entire area for US here 296 00:15:21,720 --> 00:15:24,600 Speaker 1: at Bloomberg News, Brendan, can you just paint a scene 297 00:15:24,640 --> 00:15:27,960 Speaker 1: here on what was driving the escalation that seems to 298 00:15:28,000 --> 00:15:31,160 Speaker 1: be coming to a ford in some ways? Today the 299 00:15:31,680 --> 00:15:34,720 Speaker 1: President and his advisors say that we're inching closer and 300 00:15:34,720 --> 00:15:37,880 Speaker 1: closer to a deal with China, uh, making it sound 301 00:15:37,920 --> 00:15:40,120 Speaker 1: like something was imminent and they were going to meet 302 00:15:40,320 --> 00:15:43,360 Speaker 1: meet this uh sort of a deadline of December fift 303 00:15:43,640 --> 00:15:46,360 Speaker 1: before the U s raises more tariffs on Chinese imports. 304 00:15:46,840 --> 00:15:50,480 Speaker 1: And yet the President today kind of stepped back and said, Uh, 305 00:15:50,560 --> 00:15:53,280 Speaker 1: you know, I don't really have a deadline that I'd 306 00:15:53,320 --> 00:15:56,320 Speaker 1: be fine if this drags out fully all the way 307 00:15:56,360 --> 00:15:59,040 Speaker 1: through the election of next year. So I think the 308 00:15:59,360 --> 00:16:03,120 Speaker 1: stock market reaction is definitely Uh. They were that is 309 00:16:03,160 --> 00:16:05,640 Speaker 1: that investors have been thrown for a loop here, Uh, 310 00:16:05,680 --> 00:16:07,440 Speaker 1: you know, thinking that they were close to a deal, 311 00:16:07,520 --> 00:16:09,960 Speaker 1: but now this is something that could drag on and 312 00:16:10,040 --> 00:16:12,360 Speaker 1: on for months and months. Uh you know, whether this 313 00:16:12,440 --> 00:16:15,960 Speaker 1: is just a negotiating strategy on Trump's part, the idea 314 00:16:16,040 --> 00:16:17,760 Speaker 1: being that you know, the closer you get to deal, 315 00:16:18,040 --> 00:16:19,880 Speaker 1: the more you the more you act like you don't 316 00:16:19,920 --> 00:16:22,080 Speaker 1: need it, you don't want it. Um is a whole 317 00:16:22,080 --> 00:16:25,000 Speaker 1: another question that uh you know that that is still 318 00:16:25,040 --> 00:16:27,360 Speaker 1: remains to be answered. But I just want to take 319 00:16:27,400 --> 00:16:31,080 Speaker 1: a step away here. This has been just a tariff 320 00:16:31,680 --> 00:16:33,680 Speaker 1: news cycle here over the last several days. It's not 321 00:16:33,760 --> 00:16:37,880 Speaker 1: just China. Uh we had yesterday steel tariff discussions on 322 00:16:38,600 --> 00:16:44,040 Speaker 1: Argentina and Brazil. Today it's French wine and cheese. You know, Historically, 323 00:16:44,080 --> 00:16:47,880 Speaker 1: how effective have tariff's been and has the US been 324 00:16:47,920 --> 00:16:52,880 Speaker 1: a big wielder of tariffs historically? Not in the recent past. 325 00:16:53,080 --> 00:16:56,320 Speaker 1: Obviously the Trump administration changed all that. But tariffs are 326 00:16:56,440 --> 00:17:01,880 Speaker 1: are fairly blunt instrument, used mainly as uh leverage in negotiation. 327 00:17:02,000 --> 00:17:06,679 Speaker 1: You threaten them before you actually impose them. So the 328 00:17:06,720 --> 00:17:09,000 Speaker 1: big difference that we've seen in the Trump administration is 329 00:17:09,000 --> 00:17:12,600 Speaker 1: that they impose them and then they say, Okay, let's negotiate, uh, 330 00:17:12,640 --> 00:17:14,720 Speaker 1: you know, if you want to, if you want us 331 00:17:14,720 --> 00:17:18,159 Speaker 1: to remove them. So uh they have traditionally in the 332 00:17:18,240 --> 00:17:20,080 Speaker 1: you know, in the recent in the recent past, the 333 00:17:20,119 --> 00:17:23,440 Speaker 1: past few decades, you know, uh, countries have have been 334 00:17:23,440 --> 00:17:25,960 Speaker 1: moving more and more to lower tariffs. Uh. You know, 335 00:17:26,000 --> 00:17:28,520 Speaker 1: Trump has come in and uh you know, is using 336 00:17:28,560 --> 00:17:31,880 Speaker 1: them to extract concessions from from trading partners. The interesting 337 00:17:31,920 --> 00:17:35,240 Speaker 1: thing about the French uh move that you that you 338 00:17:35,320 --> 00:17:38,560 Speaker 1: mentioned is that this could sort of bring the trade 339 00:17:38,560 --> 00:17:41,760 Speaker 1: war into Europe as a whole. The the European Union, 340 00:17:42,280 --> 00:17:45,200 Speaker 1: uh you know, uh will will have a reaction to 341 00:17:45,200 --> 00:17:48,600 Speaker 1: to that on France's behalf. And you know there's a 342 00:17:48,640 --> 00:17:51,680 Speaker 1: scenario that uh you know that you can see where 343 00:17:51,680 --> 00:17:53,800 Speaker 1: things kind of spiral out of control in this sort 344 00:17:53,840 --> 00:17:56,639 Speaker 1: of tip for tat uh way that the U. S. 345 00:17:56,760 --> 00:17:59,480 Speaker 1: China trade war has evolved, that you know, we could 346 00:17:59,560 --> 00:18:03,480 Speaker 1: wind up with two fairly large showdowns on you know, 347 00:18:03,600 --> 00:18:07,600 Speaker 1: two huge continents for a huge economic trading partners of 348 00:18:07,680 --> 00:18:10,280 Speaker 1: the US. Brendan, do you think that the headline is 349 00:18:10,440 --> 00:18:13,119 Speaker 1: the US and Europe are kind of ratcheting up the 350 00:18:13,119 --> 00:18:15,480 Speaker 1: tensions on both sides of the Atlantic, or do you 351 00:18:15,520 --> 00:18:17,320 Speaker 1: think that the headline is it could have been so 352 00:18:17,440 --> 00:18:20,320 Speaker 1: much worse and President Trump could have been going after 353 00:18:20,359 --> 00:18:24,280 Speaker 1: the auto sector for example, uh in Europe. And this 354 00:18:24,320 --> 00:18:27,880 Speaker 1: is sort of more a negotiating tactic all around as 355 00:18:27,920 --> 00:18:31,119 Speaker 1: he tries to seem powerful heading into a couple of 356 00:18:31,359 --> 00:18:34,440 Speaker 1: tough weeks. Absolutely, the car terirafs that you mentioned, that 357 00:18:34,520 --> 00:18:38,639 Speaker 1: deadline for the Trump administration to act upon came and 358 00:18:38,680 --> 00:18:44,280 Speaker 1: went without any action. A lot of people, uh economists 359 00:18:44,280 --> 00:18:48,000 Speaker 1: and auto industry experts have said that, you know, something 360 00:18:48,080 --> 00:18:51,159 Speaker 1: like that would surely uh you know send you know, 361 00:18:52,000 --> 00:18:56,080 Speaker 1: some economies like Germany into recessions. Uh you know, so, Uh, 362 00:18:56,160 --> 00:18:58,879 Speaker 1: there was a measure that there is a measured approach 363 00:18:59,720 --> 00:19:03,920 Speaker 1: it easton that way from the Trump administration. Uh. And 364 00:19:04,000 --> 00:19:07,320 Speaker 1: you know in in in reality, the two point four 365 00:19:07,359 --> 00:19:10,640 Speaker 1: billion dollars UH tariffs on two point four billion dollars 366 00:19:10,680 --> 00:19:13,080 Speaker 1: in French products is it's not a huge amount when 367 00:19:13,080 --> 00:19:15,600 Speaker 1: you consider, uh, you know, the tens of billion dollars 368 00:19:15,640 --> 00:19:18,520 Speaker 1: that the that the country's trade between themselves. Brendan, you 369 00:19:18,600 --> 00:19:22,480 Speaker 1: briefly describe what the digital service taxes and why it's 370 00:19:22,480 --> 00:19:24,840 Speaker 1: a big deal to the US government. So this is 371 00:19:24,880 --> 00:19:29,639 Speaker 1: a three percent tax on the gross revenue of of 372 00:19:30,000 --> 00:19:33,040 Speaker 1: large tech companies. Companies that make over bring in more 373 00:19:33,080 --> 00:19:35,840 Speaker 1: than seven fifty million dollars in revenue a year. This 374 00:19:35,920 --> 00:19:40,320 Speaker 1: hits companies like Google and Facebook and Amazon, and France 375 00:19:40,480 --> 00:19:44,680 Speaker 1: has has enacted that's enacted it this year. They're trying 376 00:19:44,760 --> 00:19:50,040 Speaker 1: to drive a sort of international move toward towards such attacks. 377 00:19:50,840 --> 00:19:53,920 Speaker 1: Uh and and the U. S has has has come 378 00:19:53,960 --> 00:19:57,440 Speaker 1: out against it, saying, if we're gonna tax, if American 379 00:19:57,440 --> 00:19:59,320 Speaker 1: company is going to be taxed, the U. S. Government 380 00:19:59,400 --> 00:20:00,720 Speaker 1: is going to do that, is going to do that, 381 00:20:01,040 --> 00:20:04,159 Speaker 1: not the French government. So uh, you know, in some ways, 382 00:20:04,400 --> 00:20:08,919 Speaker 1: you know, the Trump administration is acting, uh, you know, 383 00:20:09,000 --> 00:20:13,399 Speaker 1: to to defend companies that it normally doesn't defend. Uh 384 00:20:13,480 --> 00:20:16,480 Speaker 1: and and you know, and and in in this case 385 00:20:16,520 --> 00:20:18,280 Speaker 1: in particular, you know, they could have gone to the 386 00:20:18,400 --> 00:20:21,760 Speaker 1: w t O UH to dispute this, uh this tax. 387 00:20:22,000 --> 00:20:25,000 Speaker 1: Instead they're taking the you know, the the Trump strategy 388 00:20:25,080 --> 00:20:28,800 Speaker 1: of going one on one uh you know, so w 389 00:20:28,880 --> 00:20:30,760 Speaker 1: t O case could drag on for years and years. 390 00:20:30,760 --> 00:20:34,360 Speaker 1: So uh, this is this is the Trump administration strategy 391 00:20:34,440 --> 00:20:36,760 Speaker 1: is to is to uh is to fight their own fights. 392 00:20:36,920 --> 00:20:39,800 Speaker 1: Brendan Murray, thanks again so much for joining us here 393 00:20:39,800 --> 00:20:41,639 Speaker 1: and bring us up to date on all things trade. 394 00:20:42,000 --> 00:20:45,400 Speaker 1: Brendan covers the trade issue globally for Bloomberg News. Joinning 395 00:20:45,480 --> 00:20:48,760 Speaker 1: us from London and there is a lot for Brendan 396 00:20:48,920 --> 00:20:50,760 Speaker 1: and his trade team to be working on now. We 397 00:20:50,800 --> 00:20:53,480 Speaker 1: have trade discussions, tariff discussions, it seems like in every 398 00:20:54,040 --> 00:20:57,200 Speaker 1: corner of the world, and it's obviously has major impacts 399 00:20:57,200 --> 00:21:14,280 Speaker 1: on financial markets. Rates continue to be exceptionally low. The 400 00:21:14,359 --> 00:21:16,760 Speaker 1: question is, how about is it time to start looking 401 00:21:16,800 --> 00:21:19,760 Speaker 1: at and what should we expect To answer that question, 402 00:21:19,840 --> 00:21:22,440 Speaker 1: there's nobody better than our good friend, Ira Jersey, chief 403 00:21:22,640 --> 00:21:26,680 Speaker 1: US interest rate strategist for Bloomberg Intelligence. Thanks so much 404 00:21:26,720 --> 00:21:30,240 Speaker 1: for joining us. So as we think about is it 405 00:21:30,400 --> 00:21:33,880 Speaker 1: still a lower rate for longer type of outlook from 406 00:21:33,880 --> 00:21:35,920 Speaker 1: your perspective, Well, I think in the in the front 407 00:21:36,000 --> 00:21:38,439 Speaker 1: end and policy rates I think will continue to remain 408 00:21:38,520 --> 00:21:40,200 Speaker 1: kind of in the in this area. I don't think 409 00:21:40,200 --> 00:21:42,959 Speaker 1: that the Fed is likely to do anything, if at 410 00:21:42,960 --> 00:21:47,400 Speaker 1: all next year. Um Potentially they could, they could ease 411 00:21:47,440 --> 00:21:49,399 Speaker 1: policy a little bit if things get really bad on 412 00:21:49,400 --> 00:21:52,280 Speaker 1: the economic front, but I think they'll wait until after 413 00:21:52,280 --> 00:21:55,960 Speaker 1: the election to kind of reassess how things are unless 414 00:21:56,160 --> 00:21:58,560 Speaker 1: unless you see things like you know, negative payroll prints 415 00:21:58,600 --> 00:22:02,399 Speaker 1: for example, um on on. On the on the longer 416 00:22:02,480 --> 00:22:04,320 Speaker 1: term side, it's looking at like a tenure rate. I 417 00:22:04,320 --> 00:22:06,240 Speaker 1: think it's pretty clear that when you get these headlines 418 00:22:06,280 --> 00:22:09,200 Speaker 1: about trade, you wind up, uh, you know, rates winded 419 00:22:09,280 --> 00:22:11,760 Speaker 1: rawling like today ten years down eight basis points and 420 00:22:11,840 --> 00:22:14,280 Speaker 1: yield um but you know that goes away and you 421 00:22:14,320 --> 00:22:16,840 Speaker 1: wind up with probably a pretty substantial sell off and 422 00:22:16,880 --> 00:22:19,480 Speaker 1: you wind up with um tenure yields up closer to 423 00:22:19,520 --> 00:22:21,160 Speaker 1: two and a quarter instead of where they are now. 424 00:22:21,200 --> 00:22:22,720 Speaker 1: So so I think a lot of this is very 425 00:22:22,720 --> 00:22:26,520 Speaker 1: predicated on uh, kind of the balance of uncertainties remaining 426 00:22:27,600 --> 00:22:29,560 Speaker 1: remaining negative. But if you get rid of some of 427 00:22:29,600 --> 00:22:32,000 Speaker 1: those uncertainties and you know, the market can kind of 428 00:22:32,000 --> 00:22:36,680 Speaker 1: take off. Troy Gisky of Skybridge Capital is on Bloomberg 429 00:22:36,760 --> 00:22:39,520 Speaker 1: Radio earlier. We were noting that, yes, the market was 430 00:22:39,600 --> 00:22:42,200 Speaker 1: down ahead of the open, but not down by as 431 00:22:42,240 --> 00:22:45,320 Speaker 1: much as you would expect if President Trump, say in 432 00:22:45,359 --> 00:22:48,560 Speaker 1: the summer, had been saying, you know what all tariffs 433 00:22:48,600 --> 00:22:50,680 Speaker 1: are ago, who knows if we're even going to get 434 00:22:50,680 --> 00:22:52,720 Speaker 1: a deal this year or next year for that matter, 435 00:22:53,480 --> 00:22:55,760 Speaker 1: there seems to be a buffer, and he was saying 436 00:22:55,840 --> 00:22:58,520 Speaker 1: that comes in the form of the FED increasing its 437 00:22:58,520 --> 00:23:01,080 Speaker 1: balance sheet about three bill million dollars since the end 438 00:23:01,080 --> 00:23:04,359 Speaker 1: of August. How big of a support is that to valuations, 439 00:23:04,359 --> 00:23:08,120 Speaker 1: certainly in bonds. Yeah, well, well it helps a little bit. 440 00:23:08,160 --> 00:23:12,040 Speaker 1: I mean, you remember, they're buying mostly um, they're buying 441 00:23:12,040 --> 00:23:15,160 Speaker 1: mostly shortened UH debts, so they're buying mainly TE bills, 442 00:23:15,160 --> 00:23:18,200 Speaker 1: which you know don't have really a lot of market risks, 443 00:23:18,240 --> 00:23:20,879 Speaker 1: so um. So yes, it's helpful a little bit, but 444 00:23:20,960 --> 00:23:23,880 Speaker 1: it's not as meaningful as if they were going out 445 00:23:23,880 --> 00:23:26,000 Speaker 1: and buying a whole lot of you know, five, ten, 446 00:23:26,040 --> 00:23:28,600 Speaker 1: and thirty year bonds. But but that said, it actually 447 00:23:28,680 --> 00:23:34,240 Speaker 1: dampens volatility, which is a proven risk encourager, right, I 448 00:23:34,240 --> 00:23:36,600 Speaker 1: mean basically, the lower the the the volatility, the more 449 00:23:36,600 --> 00:23:38,360 Speaker 1: people will be inclined to buy stocks and by jump 450 00:23:38,400 --> 00:23:40,840 Speaker 1: bonds and going to risk. Well, I think I think 451 00:23:40,840 --> 00:23:42,480 Speaker 1: it is a little bit more of a risk on 452 00:23:42,640 --> 00:23:46,720 Speaker 1: because by buying by by buying TE bills, they're effectively 453 00:23:46,840 --> 00:23:51,280 Speaker 1: increasing the UM. They're increasing bank reserves, which which does 454 00:23:51,400 --> 00:23:54,280 Speaker 1: encourage some risk taking. UM. But but that's not what's 455 00:23:54,280 --> 00:23:56,920 Speaker 1: helping keep ten year yields low? Right, So, so there's 456 00:23:56,920 --> 00:23:59,280 Speaker 1: a difference between you know, how this might be helping 457 00:23:59,359 --> 00:24:02,320 Speaker 1: risk assets versus how it might be UM supporting or 458 00:24:02,359 --> 00:24:05,080 Speaker 1: not supporting things like ten year treasuries and and and 459 00:24:05,160 --> 00:24:08,280 Speaker 1: the like. UM. So I think the the thing that 460 00:24:09,160 --> 00:24:12,040 Speaker 1: economic fundamentals, I think matter much more to the long 461 00:24:12,119 --> 00:24:14,239 Speaker 1: end of the curve. And what you see is when 462 00:24:14,280 --> 00:24:16,880 Speaker 1: you see uncertainty and you see heightened uncertainty, you're gonna 463 00:24:17,000 --> 00:24:20,120 Speaker 1: wind up with UM regardless of what happens to say 464 00:24:20,119 --> 00:24:22,000 Speaker 1: the equity market. The equity market might go up a 465 00:24:22,040 --> 00:24:24,439 Speaker 1: little bit under the idea that's going to ease, that 466 00:24:24,440 --> 00:24:26,200 Speaker 1: interest rates are going to be low, and that might 467 00:24:26,200 --> 00:24:29,359 Speaker 1: be supportive evaluations in some risk assets. But on the 468 00:24:29,400 --> 00:24:32,080 Speaker 1: other side that the reason why that's happening is because 469 00:24:32,080 --> 00:24:36,000 Speaker 1: you have the expectation for low inflation, for for low 470 00:24:36,160 --> 00:24:40,440 Speaker 1: and and stable grow low but yet stable growth that 471 00:24:40,480 --> 00:24:43,760 Speaker 1: will keep bond yields very low. So UM because when 472 00:24:43,760 --> 00:24:45,720 Speaker 1: you look at things like really yields, so the that's 473 00:24:45,760 --> 00:24:48,520 Speaker 1: the yield on on tips and and the yield that 474 00:24:48,800 --> 00:24:52,280 Speaker 1: investors are demanding above inflation, you're only looking at that 475 00:24:52,320 --> 00:24:54,919 Speaker 1: being ten basis points over the next ten years. So 476 00:24:54,960 --> 00:24:56,760 Speaker 1: people don't think that there's going to be a lot 477 00:24:56,760 --> 00:25:01,160 Speaker 1: of volatility or particularly fast growth where you would expect 478 00:25:01,160 --> 00:25:03,040 Speaker 1: there to be a lot more risk in things like 479 00:25:03,440 --> 00:25:06,720 Speaker 1: uh inflation and inflation expectations that you demand a higher 480 00:25:06,760 --> 00:25:08,679 Speaker 1: premium for that, and you don't see that. So as 481 00:25:08,680 --> 00:25:10,960 Speaker 1: long as that remains very low, which I think is 482 00:25:11,000 --> 00:25:13,639 Speaker 1: what the trade tensions do, um, you're gonna wind up 483 00:25:13,680 --> 00:25:15,679 Speaker 1: seeing low bond deals. But again, like that can go 484 00:25:15,720 --> 00:25:17,200 Speaker 1: away in a heartbeat, and you can wind up with 485 00:25:17,200 --> 00:25:20,240 Speaker 1: a fifty basis point sell off in a hurry if 486 00:25:20,240 --> 00:25:25,399 Speaker 1: that uncertainty goes away. Presidential election year and your experience, 487 00:25:26,480 --> 00:25:29,120 Speaker 1: has there been increased volatility or the hows the bond 488 00:25:29,160 --> 00:25:32,720 Speaker 1: market typically done in presidential election years. Yeah, so so 489 00:25:33,480 --> 00:25:36,359 Speaker 1: during presidential elections, the only time that you saw a 490 00:25:36,440 --> 00:25:40,040 Speaker 1: major move after an election was really after Donald Trump's 491 00:25:40,040 --> 00:25:42,840 Speaker 1: election in two thousands sixteen. You go back, um, you 492 00:25:42,880 --> 00:25:45,040 Speaker 1: go back to the prior thirty years, so the prior 493 00:25:45,080 --> 00:25:48,320 Speaker 1: six elections, and you really did not have significant market 494 00:25:48,359 --> 00:25:50,679 Speaker 1: reaction one way or the other. It tended to be 495 00:25:50,720 --> 00:25:53,200 Speaker 1: whatever the trend was going into that based on economic 496 00:25:53,240 --> 00:25:57,359 Speaker 1: fundamentals is what continued to drive the bond market. But Steen, 497 00:25:57,400 --> 00:25:59,480 Speaker 1: you know that that was a significant change, and you 498 00:25:59,520 --> 00:26:02,160 Speaker 1: know this this year, maybe you could see something similar 499 00:26:02,200 --> 00:26:04,280 Speaker 1: if you've got to had a candidate win that you 500 00:26:04,280 --> 00:26:06,800 Speaker 1: know was going to either, uh, you know, change policy 501 00:26:06,880 --> 00:26:09,040 Speaker 1: quite significantly, and you know we'd have to see who 502 00:26:09,119 --> 00:26:10,600 Speaker 1: that was before you can make a guess us to 503 00:26:10,640 --> 00:26:13,240 Speaker 1: which direction the bond market would move. Our Jersey. Thank 504 00:26:13,280 --> 00:26:15,880 Speaker 1: you so much for being with us. Our Jersey covers 505 00:26:15,920 --> 00:26:18,080 Speaker 1: all things interest rates for us as chief US interest 506 00:26:18,119 --> 00:26:21,159 Speaker 1: rate strategist for Bloomberg Intelligence. Thanks for listening to the 507 00:26:21,200 --> 00:26:23,919 Speaker 1: Bloomberg pen L podcast. You can subscribe and listen to 508 00:26:23,920 --> 00:26:27,200 Speaker 1: interviews at Apple Podcasts or whatever podcast platform you prefer. 509 00:26:27,560 --> 00:26:30,320 Speaker 1: Paul Sweeney, I'm on Twitter at pt Sweeney. I'm Lisa 510 00:26:30,440 --> 00:26:33,480 Speaker 1: bram Woods. I'm on Twitter at Lisa bramwods one. Before 511 00:26:33,480 --> 00:26:36,679 Speaker 1: the podcast, you can always catch us worldwide on Bloomberg Radio.